Methods and systems for redistributing medication

ABSTRACT

A system for redistributing medication including a computing device configured to receive a communication regarding a medication donation from a donor, wherein the communication includes donor medication information regarding the medication donation. The computing device further configured to verify the medication donation including verifying the identity of the donated medication. Verifying the identity of the donated medication includes collecting actual medication information. The computing device also configured to enter final medication information corresponding to the medication donation into the medication database. The computing device configured to match the donated medication to a recipient selected from a recipient database as a function of a set of associated recipient data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of Non-provisionalapplication Ser. No. 17/575,792 filed on Jan. 14, 2022 and entitled“METHODS AND SYSTEMS FOR REDISTRIBUTING MEDICATION,” the entirety ofwhich is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of medicationmanagement. In particular, the present invention is directed to methodsand systems for redistributing medication.

BACKGROUND

It is important to medical treatment to ensure that patients are able toreceive the correct medication when they need it. If the patient is notable to receive the correct medication, it may have dire consequencesfor their health. Thus, unused medication represents wasted potential,where the unused medication could be more useful if it was being used totreat a patient in need. Existing solutions for redistributingmedication do not adequately resolve this problem.

SUMMARY OF THE DISCLOSURE

In an aspect, a system for redistributing medication, the systemincluding a computing device designed and configured to receive acommunication regarding a medication donation from a donor, wherein thecommunication includes donor medication information regarding themedication donation and verify the medication donation as a function ofactual medication information. Verifying the medication donationincludes collecting the actual medication information for the medicationdonation using a sensor, wherein the sensor includes a camera, verifyingthe identity of the medication donation, and verifying the integrity ofthe medication donation. The computing device further configured toenter the actual medication information for the medication donation intothe medication database as a function of verifying the medicationdonation.

In another aspect, a method for redistributing medication, the methodincluding receiving, by the computing device, a communication regardinga medication donation from a donor, wherein the communication includesdonor medication information regarding the medication donation. Themethod further comprising verifying, by the computing device, themedication donation as a function of actual medication information,wherein verifying the medication donation includes collecting the actualmedication information for the medication donation using a sensor,wherein the sensor includes a camera, verifying the identity of themedication donation, and verifying the integrity of the medicationdonation. The method further includes entering, by the computing device,the actual medication information for the medication donation into themedication database as a function of verifying the medication donation.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a diagram of a system for redistributing medication;

FIG. 2 is a diagram of an exemplary embodiment of a computing device;

FIG. 3 is a flow chart of an exemplary method for verifying medicationdonations;

FIG. 4 is an illustration of exemplary packaging for a medicationdonation;

FIG. 5 is an illustration of an exemplary first medication pill andsecond medication pill;

FIG. 6 is a flow chart for a method for redistributing medicine;

FIG. 7 is a diagram of an exemplary embodiment of an immutablesequential listing;

FIG. 8 is a diagram of an exemplary neural network;

FIG. 9 is a diagram of an exemplary node of a neural network;

FIG. 10 is a diagram of an exemplary machine learning module; and

FIG. 11 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for redistributing medicine. In an embodiment,recipients from a recipient database may be matched to medicationdonation. In some embodiments, this may involve a matching machinelearning model. This matching may be based on, for example, recipientdata from the recipient database.

Aspects of the present disclosure can be used for verifying medicationdonations. Aspects of the present disclosure can also be used to verifythe identity of a medication donation. This may be done for example,using a machine learning algorithm. In some embodiments, a pharmacistmay verify the identity of the medication donation. In some aspects ofthe present disclosure, verifying medication donations may includeverifying the integrity of a medication donation.

Aspects of the present disclosure allow for a verification record to bestored using an immutable sequential listing. This bolsters theintegrity of the verification record as it makes it harder for theverification record to be falsified or otherwise tampered with.

Referring now to FIG. 1 , a system for redistributing medication 100 isshown. System 100 includes a computing device 104. System includes acomputing device. computing device may include any computing device asdescribed in this disclosure, including without limitation amicrocontroller, microprocessor, digital signal processor (DSP) and/orsystem on a chip (SoC) as described in this disclosure. Computing devicemay include, be included in, and/or communicate with a mobile devicesuch as a mobile telephone or smartphone. Computing device may include asingle computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Computingdevice may interface or communicate with one or more additional devicesas described below in further detail via a network interface device.Network interface device may be utilized for connecting computing deviceto one or more of a variety of networks, and one or more devices.Examples of a network interface device include, but are not limited to,a network interface card (e.g., a mobile network interface card, a LANcard), a modem, and any combination thereof. Examples of a networkinclude, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device.computing device may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Computing device may include one or more computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and the like. Computing device may distribute one or morecomputing tasks as described below across a plurality of computingdevices of computing device, which may operate in parallel, in series,redundantly, or in any other manner used for distribution of tasks ormemory between computing devices. computing device may be implementedusing a “shared nothing” architecture in which data is cached at theworker, in an embodiment, this may enable scalability of system 100and/or computing device.

With continued reference to FIG. 1 , computing device may be designedand/or configured to perform any method, method step, or sequence ofmethod steps in any embodiment described in this disclosure, in anyorder and with any degree of repetition. For instance, computing devicemay be configured to perform a single step or sequence repeatedly untila desired or commanded outcome is achieved; repetition of a step or asequence of steps may be performed iteratively and/or recursively usingoutputs of previous repetitions as inputs to subsequent repetitions,aggregating inputs and/or outputs of repetitions to produce an aggregateresult, reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. computing device mayperform any step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

With continued reference to FIG. 1 , computing device 104 is configuredand arranged to receive a communication 108 regarding a medicationdonation from a donor. Communication 108 may be received via wireless orwired communication. As non-limiting examples, wireless communicationmay be WiFi, cellular data, 3G, 4G, 5G, LTE, radio communication,satellite communication, and the like. Communication 108 may contain avariety of information regarding the medication donation. In anembodiment, communication 108 may contain information about the law suchas appropriate medication dispensing laws, appropriate medicationstorage information, appropriate medical record information, appropriatepharmacy regulations, legislation and/or rules that authorizerepurposing and/or donating medications, and the like. In an embodiment,communication 108 may contain donor medication information concerningthe medication in the medication donation. As non-limiting example, thisinformation may include quantity of medication, type of medication,expiration date of medication, brand of medication, and the like. Insome embodiments, communication 108 may contain geographic informationregarding the medication donation. As non-limiting examples, this mayinclude the state where the medication donation is, the organizationthat has possession of the medication donation, the individual who haspossession of the medication donation, the address of the location ofthe medication donation, and the like. In some embodiments,communication 108 may include donor information. As non-limitingexamples, this may include, the name of the donor, the age of the donor,whether the donor is an institutional donor, and the like. One ofordinary skill in the art, after having reviewed the entirety of thisdisclosure, would appreciate that communication 108 may include avariety of information concerning the medication donation. For thepurposes of this disclosure, a “medication donation” is a portion, part,or group of unused medication that is to be redistributed. For thepurposes of this disclosure, “medication” is a drug used to prevent,cure, treat, diagnose, alleviate, or contain a disease, symptoms,conditions, or nutritional deficiency. As a non-limiting example,medication may include blood pressure medications, such as Valsartan,Metoprolol, Olmesartan, and the like. As another non-limiting example,medication may include high-cholesterol medication, such asAtorvastatin, Fluvastatin, Lovastatin, and the like. As anothernon-limiting example, medication may include various types of vitamins,such as Vitamin D, a multivitamin, Vitamin B3, Calcium, and the like. Asanother non-limiting embodiment, medication may include immunizations,such as an influenza vaccine, an MMR combined vaccine, a COVID-19vaccine, and the like. As another non-limiting example, medication mayinclude preventative medications, such as Atovaquone-proguanil. In someembodiments, a single medication may include one or more activeingredients. In some embodiments, medication may include a medicationregimen, including more than one medication. For the purposes of thisdisclosure a “donor” is the person or entity that is disposing of,donating, or selling the medication donation. In some embodiments, thedonor may receive compensation for the medication donation. In someother embodiments, the donor may receive no compensation for themedication.

With continued reference to FIG. 1 , in some embodiments, computingdevice 104 may be designed and configured to generate a shipping label112 for the medication donation. In some embodiments, generating ashipping label 112 for the medication donation may be a part ofreceiving a communication regarding a medication donation from a donor.In some embodiments, shipping label 112 may be generated automatically,for example, as a result of computing device 104 receiving communication108. In some embodiments, shipping label 112 may be generated as aresult of a command from donor, or from any user of computing device104. Shipping label 112 may contain a variety of information; forexample, an address, a return address, a barcode, and the like.Generating shipping label 112 may include interfacing with a third-partyapplication programming interface (API). In some embodiments, generatingshipping label 112 may include sending shipping label 112 to the donor.As a non-limiting example, sending shipping label 112 to the donor mayinclude sending shipping label 112 using a postal service, parcelservice, or currier service. As a non-limiting example, sending shippinglabel 112 to the donor may include sending the shopping label (or a linkto shipping label 112) by electronic means; for example, email, instantmessage, social media message, Short Message Service (SMS), MultimediaMessaging Service (MMS), Bluetooth, and the like.

With continued reference to FIG. 1 , computing device 104 is designedand configured to verify the medication donation wherein verifying themedication donation includes verifying the identity of the medicationdonation. Verifying the identity of the medication donation includescollecting actual medication information 116 from the medicationdonation. Actual medication information 116 may include any informationcollected from the medication donation itself. As a non-limitingexample, actual medication information may include data collected fromthe medication donation, such as chemical composition, shape, color,strength, expiration date, efficacy, dosages left, and the like. As anon-limiting example, actual mediation information may include records,such as images or videos, of the medication donation. Images and videosof the medication donation, as non-limiting examples, may be of themedication donation packaging, a pill of the medication donation, a unitof the medication donation, a dose of the medication donation, and thelike.

Continuing to refer to FIG. 1 , verifying the identity of the medicationdonation may include using an identification machine learning model toverify the identity of the medication donation. The identificationmachine learning model may be trained to verify the identity of themedication donation by comparing the donor medication information incommunication with the actual medication information 116. For thepurposes of this disclosure, the “identity of the medication donation”refers to the generic name, brand name, quantity of medication, dosageof medication, or chemical composition of the medication. In someembodiments, identification machine learning model may output a donationidentity verification. In some embodiments, the donation identityverification may be a binary value. As a non-limiting example, “0” mayrepresent a failed verification, whereas “1” may represent a successfulverification. In some embodiments, donation identity verification mayrepresent a confidence in the verification. Identification machinelearning model may be implemented using a machine learning module, asdiscussed in this disclosure.

Continuing to refer to FIG. 1 , in some embodiments, verifying themedication donation may include verifying the integrity of themedication donation. For the purposes of this disclosure, the“integrity” of the medication donation, refers to whether or not themedication donation is still suitable for its intended purpose. As anon-limiting example, this may include chemical integrity. As anon-limiting example verifying the integrity of the medication donationmay include verifying that the chemical content of the medicationdonation is proper. As a non-limiting example, a medication donation mayno longer be suitable for its intended purpose is it has expired. Asanother non-limiting example, a medication may no longer be suitable forits intended purpose if it has been tampered with. In some embodiments,verifying the integrity of the medication donation may include receivingan inspection report from a pharmacist. For the purposes of thisdisclosure, a “pharmacist” is a health professional that providespharmaceutical care and/or services. In some embodiments, thepharmacist, in preparing the report, may certify intact tamper evidentseals and check the expiration date. In some embodiments, the inspectionreport may also verify the identity of the medication donation. Forexample, the pharmacist may inspect each pill in the medicationdonation, verify drug identity, and check correct dosage.

With continued reference to FIG. 1 , in some embodiments, verifying themedication donation may include checking the medication donation fortampering. For the purposes of this disclosure, “tampering” in relationto a medication donation, is the unauthorized altercation orsubstitution of a medication in a medication donation. Checking themedication donation for tampering may include verifying the identity ofthe medication in the medication donation. For example, differentcomponents of the medication donation (such as pills, capsules, packs,and the like) may be compared against each other to ensure that themedication donation is uniform. In some embodiments, as a non-limitingexample, each pill in a medication donation may be checked against everyother pill in the medication donation to ensure uniformity. In someembodiments, as another non-limiting example, a statistically relevantsample of the pills in a medication donation may be checked foruniformity. Therefore, in this example, uniformity in the statisticallyrelevant sample can be used as a proxy for uniformity of the entiremedication donation. For the purposes of this disclosure, a“statistically relevant sample” is a sample of a size great enough toallow inferences about a whole to be drawn from the sample. In someembodiments, verifying the identity of the medication donation mayinclude comparing identifying information between different parts of themedication donation. For example, this may include comparing theidentifying information on the outer packaging on the medicationdonation to identifying information on an interior packaging of themedication donation. As a non-limiting example, this may includecomparing an expiration date and lot number on a box of a medicationdonation to an expiration date and lot number on the back of eachblister pack in the medication donation.

With continued reference to FIG. 1 , in some embodiments, the medicationdonation for tampering may include verifying the integrity of themedication donation. In some embodiments, this may include checking themedication donation for any damage, such as dents, cracks, chips, andthe like—either on the packaging of the medication donation, or on themedication of the medication donation itself. In some embodiments,verifying the integrity of the medication donation may include checkingany seals of the medication donation. As non-limiting examples, this mayinclude checking for wholes in a shrink wrap packaging, checking forholes in a blister pack, and the like.

With continued reference to FIG. 1 , verifying the medication donationmay further include selecting, optionally, the medication donation forfurther verification testing. In an embodiment, “selecting, optionally,the medication donation” may include randomly selecting certainmedication donations. As a non-limiting example, this may beaccomplished using the output of a random number generator. By changingwhat output from the random number generator causes the medicationdonation to undergo further verification testing, the probability of agiven medication donation being selected can be adjusted. In someembodiments, “selecting, optionally, the medication donation” mayinclude selecting every n^(th) medication donation, wherein n is aninteger above zero. As a non-limiting example, where n=100 every100^(th) medication donation may be selected for further verificationtesting. In some embodiments, a statistically relevant sample ofmedication donations may be selected for further verification testing.As a non-limiting example, a statistically relevant sample of medicationdonation may be selected for further verification testing, as a proxyfor selecting all of the medication donations for further verificationtesting. For the purposes of this disclosure, a sample is “statisticallyrelevant” if the sample is large enough that, under the principles ofstatistics, the properties of the sample can reasonably be extrapolatedto the whole. In some embodiments, further verification testing mayinclude further chemical testing. In some embodiments, selecting,optionally, the medication donation for further verification testing mayinclude sending the medication donation to a third-party chemicalanalytic company.

With continued reference to FIG. 1 , in some embodiments, verifying themedication donation may comprise creating a verification record 120including a verification status of the medication donation, wherein theverification record 120 is sored using an immutable sequential listing.An “immutable sequential listing,” as used in this disclosure, is a datastructure that places data entries in a fixed sequential arrangement,such as a temporal sequence of entries and/or blocks thereof, where thesequential arrangement, once established, cannot be altered orreordered. An immutable sequential listing may be, include and/orimplement an immutable ledger, where data entries that have been postedto the immutable sequential listing cannot be altered. The verificationrecord 120 may include a plurality of entries for a plurality ofmedication donations. In some embodiments, the verification record 120may include additional information regarding the medication donation. Asa non-limiting example, the verification record 120 may also include averification failure reason. The verification failure reason may be areason why the medication donation failed verification. As anon-limiting example, this may include that the medication donation hadexpired, or that the medication donation did not match the donormedication information from communication 108. A person of ordinaryskill in the art, having reviewed the entirety of this disclosure, wouldrealize that there is a variety of additional information regarding themedication donation may be included in the verification record 120. Theconcept of an immutable sequential listing is discussed further withrespect to FIG. 7 .

With continued reference to FIG. 1 , in some embodiments, verifying themedication donation may comprise checking the medication donation fortampering and creating a verification record 120 including averification status for the medication donation and/or a tamperingstatus for the medication donation. In some embodiments, verifying themedication donation may comprise determining a tampering status. For thepurposes of this disclosure, a “tampering status” is a status indicatingwhether or not the medication donation has been subject to tampering.Tampering may be detected using any method disclosed in this disclosure,including but not limited to the use of a machine vision system and/orimage classifier. In some embodiments, the verification record 120 mayinclude a plurality of entries for a plurality of medication donations,wherein each of the plurality of entries includes a tampering status. Insome embodiments, the tampering status may be stored in verificationrecord 120 on an immutable sequential listing.

With continued reference to FIG. 1 , computing device 104 is designedand configured to enter final medication information corresponding tothe medication donation into the medication database 124. In someembodiments, final medication information may include a combination ofdata from the donor medication information and actual medicationinformation 116. In some embodiments, final medication information mayinclude at least a portion of actual medication information 116. In someembodiments, medication database 124 may be a database with entriescorresponding to each medication donation. As a non-limiting example,each entry corresponding to a medication donation may be associated withone or more components corresponding to one or more data categories. Theone or more data categories may include any category of data included indonor medication information or actual medication information 116. Insome embodiments, medication database 124 may include entries for aplurality of types of medication. As a non-limiting example, in theseembodiments, a “type of medication” may be the generic name for themedication. In these embodiments, the corresponding components of aplurality of final medication information corresponding to the same typeof medication, may be concatenated together. Medication database 124 maybe implemented, without limitation, as a relational database, akey-value retrieval database such as a NOSQL database, or any otherformat or structure for use as a medication database 124 that a personskilled in the art would recognize as suitable upon review of theentirety of this disclosure. Medication database 124 may alternativelyor additionally be implemented using a distributed data storage protocoland/or data structure, such as a distributed hash table or the like.Medication database 124 may include a plurality of data entries and/orrecords as described above. Data entries in a medication database 124may be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina medication database 124 may store, retrieve, organize, and/or reflectdata and/or records as used herein, as well as categories and/orpopulations of data consistently with this disclosure. In someembodiments, a credential may be required in order for computing device104 to access the medication database 124. As non-limiting examples, acredential may include a license, authority, digital credential,password, certificate, and the like.

Still referring to FIG. 1 , system 100 may include a recipient database128. Recipient database 128 may include a plurality of entries for aplurality of recipients for medication donations. Each entry for arecipient may include an associated set of recipient data. “Recipientdata,” for the purposes of this disclosure is data corresponding to arecipient. Recipient database 128 may be implemented, withoutlimitation, as a relational database, a key-value retrieval databasesuch as a NOSQL database, or any other format or structure for use as adatabase that a person skilled in the art would recognize as suitableupon review of the entirety of this disclosure. Recipient database 128may alternatively or additionally be implemented using a distributeddata storage protocol and/or data structure, such as a distributed hashtable or the like. Recipient database 128 may include a plurality ofdata entries and/or records as described above. Data entries in adatabase may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a database may store, retrieve, organize, and/or reflect dataand/or records as used herein, as well as categories and/or populationsof data consistently with this disclosure. In some embodiments, acredential may be required in order for computing device 104 to accessthe recipient database 128. As non-limiting examples, a credential mayinclude a license, authority, digital credential, password, certificate,and the like.

Still referring to FIG. 1 , in some embodiments, system 100 may includea public medication database. The public medication database may includemedication information corresponding to medication donations. The publicmedication database may be publicly accessible, meaning that a member ofthe public can access and browse the database. In some embodiments, amember of the public may need to register in order to view the publicdatabase. As a non-limiting example, a member of the public may need tocreate a username and password in order to view the public database. Inother embodiments, the public database may be accessible to members ofthe public regardless of whether or not they have registered. In someembodiments, users be asked to provide a National Provider Identifier(NPI) number in order to access public database. The NPI number may bein congruence with the NPI standard. As a non-limiting example, an NPInumber may be a 10-position, intelligence-free numeric identifier.Public medication database may be implemented, without limitation, as arelational database, a key-value retrieval database such as a NOSQLdatabase, or any other format or structure for use as a database that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. Public medication database mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Public medication database may include a plurality of dataentries and/or records as described above. Data entries in a databasemay be flagged with or linked to one or more additional elements ofinformation, which may be reflected in data entry cells and/or in linkedtables such as tables related by one or more indices in a relationaldatabase. Persons skilled in the art, upon reviewing the entirety ofthis disclosure, will be aware of various ways in which data entries ina database may store, retrieve, organize, and/or reflect data and/orrecords as used herein, as well as categories and/or populations of dataconsistently with this disclosure.

Still referring to FIG. 1 , computing device 104 may be designed andconfigured to match the medication donation to a recipient selected fromrecipient database 128 as a function of a set of recipient data, whereineach recipient in the recipient database 128 has an associated set ofrecipient data. In some embodiments, computing device 104 may performthis matching process by isolating a particular component of the set ofrecipient data. As a non-limiting example, where a component of the setof recipient data is the quantity of medication remaining for theassociated recipient, computing device 104 may match the medicationdonation to the recipient with the lowest quantity of medicationremaining. In some embodiments, computing device 104 may perform thismatching process by assigning weights to certain components in the setof recipient data and matching the medication donation to the recipientwith the highest combined value of the weighted components. In someembodiments, the match of the medication donation and the recipient maybe stored in recipient database 128. As a non-limiting example, thematch may be stored as a component of the recipient data in recipientdatabase 128.

With continued reference to FIG. 1 , in some embodiments, computingdevice 104 may be designed and configured to match the medicationdonation to a recipient selected from recipient database 128 using amatching machine learning model, wherein the matching machine learningmodel that takes medication information from the medication database asinput and outputs at least a recipient from the recipient database. Insome embodiments, computing device 104 may be designed and configured totrain the machine learning model using training data 132. Matchingmachine learning model may be implemented using a machine learningmodule, as described in this disclosure. In some embodiments, matchingmachine learning module may be a neural network. Neural networks arediscussed further with respect to FIGS. 8 and 9 . “Training data,” asused herein, is data containing correlations that a machine-learningprocess, such as a classifier, may use to model relationships betweentwo or more categories of data elements. For instance, and withoutlimitation, training data may include a plurality of data entries, eachentry representing a set of data elements that were recorded, received,and/or generated together; data elements may be correlated by sharedexistence in a given data entry, by proximity in a given data entry, orthe like. Training data 132 may include, in some embodiments, priorpurchasing data for recipients, prior medication purchasing data, priormedication prescribing data, prior medication need data, and the like.

With continued reference to FIG. 1 , computing device 104 may bedesigned and configured to remove the medication donation informationcorresponding to a medication donation from the medication database 124when a buffer time period before an expiration date of the medicationdonation is reached. In some embodiments, buffer time period may includeany number above and including zero. In some embodiments, buffer timeperiod may be expressed in a unit of time, such as, for example,seconds, minutes, hours, days, weeks, months, years, and the like. As anon-limiting example, where the buffer time period is two weeks,computing device 104 may be designed and configured to remove themedication donation information corresponding to a medication donationfrom the medication database 124 two weeks before the expiration date ofthe medication donation. For the purposes of this disclosure, an“expiration date” is a previously determined date, after which the itemhaving the expiration date should no longer be used. As anothernon-limiting example, where the buffer time period is zero days, themedication donation may be removed from medication database 124 bycomputing device 104 on the expiration date associated with thatmedication donation. In some embodiments, buffer time period may applyglobally to all medication donations. In some embodiments, buffer timeperiod may be set on an individual basis for each medication donation.In some embodiments, buffer time period may be set on an individualbasis for each type of medication. In some embodiments, computing device104 may be configured to remove the medication donation informationcorresponding to a medication donation from the medication database 124if medication donation has been tampered with. As a non-limitingexample, medication donation information corresponding to a medicationdonation may be removed from the medication data 124 if the tamperingstatus for that medication donation indicates that it has been tamperedwith. In some embodiments, removing the medication donation informationcorresponding to a medication donation from the medication database 124when a buffer time period before an expiration date of the medicationdonation is reached may include moving the medication donationinformation to an expired medication database. Expired medicationdatabase may include information relating to medication donations thathave expired. Expired medication database may be consistent with aspectsof any database disclosed as part of this disclosure.

Still referring to FIG. 1 , in some embodiments, computing device 104may be designed and configured to verify the identity of the donor usingat least a donor credential 136 from the donor. In some embodiments,computing device may be configured to receive a donor credential 136.For the purposes of this disclosure, a “credential” is an item used toestablish a person or entity's identity or qualifications. As anon-limiting example, donor credential 136 may include a password,passcode, secret phrase, or the like. As another non-limiting example,donor credential 136 may include a form of identification, such as an IDcard, passport, driver's license, and the like. As another non-limitingexample, donor credential 136 may include a certification document,showing that donor is certified to be a medication donor. In someembodiments, credential may be a string of characters, in otherembodiments, it may be a picture or scan of a physical credential.Computing device 104 may use a machine learning algorithm that takes asinput images, pdfs, pngs, and the like and outputs an identityverification result. In some embodiments, computing device 104 mayaccomplish this using a machine learning module, as discussed later inthis disclosure. Identity verification result may be a binary valueindicating whether identity verification was successful or unsuccessful.In some embodiments, identity verification result may be a valueindicating the probability that the identity of donor is correct. Insome embodiments, computing device 104 may verify the identity of thedonor using at least a donor credential 136 using an imageclassification module, as discussed later in this disclosure. In someembodiments, as part of verifying the identity of the donor, computingdevice 104 may perform optical character recognition (OCR), using, insome embodiments, an OCR module, as discussed later in this disclosure.

With continued reference to FIG. 1 , computing device 104, may bedesigned and configured to identify a central repository to store themedication donation. For the purposes of this disclosure, a “centralrepository” is a storage location for medication donations. As anon-limiting example, central repository may be a warehouse, storeroom,depository, stockroom, and the like. In some embodiments, the centralrepository may be part of a larger entity. As a non-limiting example,central location may be part of a hospital or pharmacy. In someembodiments, storage within central repository may be done by anorganizational methodology, such as by storing medications in alphabeticorder, by brand name, by generic name and the like. In some embodiments,storage may be chaotic and without any organizational methodology. Insome embodiments, storage in a virtual repository may mirror and/or bein alignment with a central repository. In some embodiments, computingdevice 104 may identify a central repository by locating the closestcentral repository to the medication donation. This may involve, as anon-limiting example, accessing a central repository database 140.Central repository database 140 may, in some embodiments, includeentries for a plurality of central repositories. In some embodiments,each central repository entry for the plurality of central repositoriesmay be associated with central repository data pertaining to thatcentral repository. Central repository data may include any datarelevant to that central repository. As non-limiting examples, centralrepository data may include an address for the central repository, theclimate of the central repository, the amenities of the centralrepository, and the like. In some embodiments, computing device may usea central repository machine learning model to identify the centralrepository. Central repository machine learning model may be implementedusing a machine learning module, as discussed in this disclosure. Insome embodiments, central repository machine learning model may be aneural network. Neural networks are discussed further with respect toFIGS. 8 and 9 . In some embodiments, central repository machine learningmodel may take as input data from recipient database 128 and/or centralrepository database 140. In some embodiments, central repository machinelearning model may output a central repository identification, whereinthe central repository identification includes a central repository. Asa non-limiting example, central repository machine learning model may beconfigured to identify the central repository based on the climate ofthe central repository (for storing the medication donation) and thedemand for the medication donation in the geographic area of the centralrepository (for example, using recipient database 128). For the purposesof this disclosure, “geographic area” is a demarcated area of the earth.As a non-limiting example, geographic area may include a town, a city, acounty, a region, a metropolitan area, a city, a state, a country, andthe like. In some embodiments, a geographic area may include a circularregion surrounding a central repository. For example, a 100-mile regionsurrounding a central repository. In some embodiments, geographic areasmay include one or more mailing zones. Mailing zones may include postalzones, zip codes, postal codes, and the like. In some embodiments,postal zones may be defined using a distance from a shipping originpoint. In other embodiments, a geographic area may be defined relativeto the positions of all of the central repositories. For example, afirst central repository may be part of a geographic area, wherein thegeographic area is the area of the earth that is closer to the firstcentral repository than it is to any other central repository. Centralrepository database 140 may be implemented, without limitation, as arelational database, a key-value retrieval database such as a NOSQLdatabase, or any other format or structure for use as a database that aperson skilled in the art would recognize as suitable upon review of theentirety of this disclosure. Central repository database 140 mayalternatively or additionally be implemented using a distributed datastorage protocol and/or data structure, such as a distributed hash tableor the like. Central repository database 140 may include a plurality ofdata entries and/or records as described above. Data entries in adatabase may be flagged with or linked to one or more additionalelements of information, which may be reflected in data entry cellsand/or in linked tables such as tables related by one or more indices ina relational database. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in which dataentries in a database may store, retrieve, organize, and/or reflect dataand/or records as used herein, as well as categories and/or populationsof data consistently with this disclosure.

With continued reference to FIG. 1 , medication donation may undergo anisolation process. In some embodiments, the isolation process mayinclude three steps. In the first step, the medication donation may beheld in an isolation area. The isolation area is different from thecentral repository. As part of the first step, the medication donationmay be held in an isolation area until the identity of the medicationhas been verified. As a non-limiting example, this may include verifyingthe correct packaging and medication type. As part of the first step,the medication donation may be held in an isolation area until theexpiration date of the medication donation has been verified. Theisolation process may include a second step, wherein the medicationdonation is sent to a central repository. In some embodiments, thecentral repository may be the central repository identified by computingdevice 104, as discussed above. While at the central repository, themedication donation may undergo further testing such as chemicalanalysis, inspection by a registered pharmacist, machine visionanalysis, or any other medication testing process discussed in thisdisclosure. In some embodiments, a medication donation may be selectedto undergo further testing, such as the process disclosed with referenceto FIG. 3 , if computing device 104 has detected tampering with themedication donation.

With continued reference to FIG. 1 , in some embodiments, the system mayinclude a sensor 144. Sensor 144 may be used to collect actualmedication information. For the purposes of this disclosure, a “sensor”is a device (or devices) that is configured to detect a phenomenon andtransmit information and/or datum related to the detection of thephenomenon. In some embodiments, sensor 144 may include a camera. Asused in this disclosure, a “camera” is a device that is configured tosense electromagnetic radiation, such as without limitation visiblelight, and generate an image representing the electromagnetic radiation.In some cases, a camera may include one or more optics. Exemplarynon-limiting optics include spherical lenses, aspherical lenses,reflectors, polarizers, filters, windows, aperture stops, and the like.In some cases, at least a camera may include an image sensor. Exemplarynon-limiting image sensors include digital image sensors, such aswithout limitation charge-coupled device (CCD) sensors and complimentarymetal-oxide-semiconductor (CMOS) sensors, chemical image sensors, andanalog image sensors, such as without limitation film. In some cases, acamera may be sensitive within a non-visible range of electromagneticradiation, such as without limitation infrared. As used in thisdisclosure, “image data” is information representing at least a physicalscene, space, and/or object. In some cases, image data may be generatedby a camera. “Image data” may be used interchangeably through thisdisclosure with “image,” where image is used as a noun. An image may beoptical, such as without limitation where at least an optic is used togenerate an image of an object. An image may be material, such aswithout limitation when film is used to capture an image. An image maybe digital, such as without limitation when represented as a bitmap.Alternatively, an image may be comprised of any media capable ofrepresenting a physical scene, space, and/or object. Alternatively,where “image” is used as a verb, in this disclosure, it refers togeneration and/or formation of an image. In some embodiments, using thesensor 144 to collect actual medication information 116 may includereceiving an image from the camera. As part of collecting actualmedication information, in some embodiments, an image classifier may beused by computing device 104 to classify the image transmitted by thecamera. Image classifiers are disclosed further with reference to FIG. 2.

With continued reference to FIG. 1 , in some embodiments, system 100 maybe configured to use sensor 144 to detect tampering with the medicationdonation. In various embodiments, this may occur during or after actualmedication information 116 is collected. As a non-limiting example,sensor 144 may collect images of a plurality of medication (pills,capsules, tablets, and the like) and compare them against one another.In some embodiments, computing device 104 may be used to classify theimages collected by sensor 144 using an image classifier as disclosedwith reference to FIG. 2 . In some embodiments, sensor 144 may capture afirst image of an exterior packaging of the medication donation. In someembodiments, sensor 144 may capture a second image of an interiorpackaging of the medication donation. Computing device 104 may collect afirst set of medication information from the first image and/or collecta second set of medication information from the second image. Thisinformation may be collected in a manner consistent with the collectionof actual medication information as discussed throughout thisdisclosure. In some embodiments, computing device 104 may compare thefirst set of information to the second set of information. In someembodiments, computing device 104 may determine whether the medicationdonation has been subject to tampering as a function of the comparison.As a non-limiting example, a mismatch between the first data set and thesecond data set may indicate that the medication donation has beentampered with.

With continued reference to FIG. 1 , in embodiments where sensor 144includes a camera, system 100 may include a machine vision system. Amachine vision system may use images from at least a camera, to make adetermination about a scene, space, and/or object. For example, in somecases a machine vision system may be used for world modeling orregistration of objects within a space. In some cases, registration mayinclude image processing, such as without limitation object recognition,feature detection, edge/corner detection, and the like. Non-limitingexamples of feature detection may include scale invariant featuretransform (SIFT), Canny edge detection, Shi Tomasi corner detection, andthe like. In some cases, registration may include one or moretransformations to orient a camera frame (or an image or video stream)relative a three-dimensional coordinate system; exemplarytransformations include without limitation homography transforms andaffine transforms. In an embodiment, registration of first frame to acoordinate system may be verified and/or corrected using objectidentification and/or computer vision, as described above. For instance,and without limitation, an initial registration to two dimensions,represented for instance as registration to the x and y coordinates, maybe performed using a two-dimensional projection of points in threedimensions onto a first frame, however. A third dimension ofregistration, representing depth and/or a z axis, may be detected bycomparison of two frames; for instance, where first frame includes apair of frames captured using a pair of cameras (e.g., stereoscopiccamera also referred to in this disclosure as stereo-camera), imagerecognition and/or edge detection software may be used to detect a pairof stereoscopic views of images of an object; two stereoscopic views maybe compared to derive z-axis values of points on object permitting, forinstance, derivation of further z-axis points within and/or around theobject using interpolation. This may be repeated with multiple objectsin field of view, including without limitation environmental features ofinterest identified by object classifier and/or indicated by anoperator. In an embodiment, x and y axes may be chosen to span a planecommon to two cameras used for stereoscopic image capturing and/or an xyplane of a first frame; a result, x and y translational components and ϕmay be pre-populated in translational and rotational matrices, foraffine transformation of coordinates of object, also as described above.Initial x and y coordinates and/or guesses at transformational matricesmay alternatively or additionally be performed between first frame andsecond frame, as described above. For each point of a plurality ofpoints on object and/or edge and/or edges of object as described above,x and y coordinates of a first stereoscopic frame may be populated, withan initial estimate of z coordinates based, for instance, on assumptionsabout object, such as an assumption that ground is substantiallyparallel to an xy plane as selected above. Z coordinates, and/or x, y,and z coordinates, registered using image capturing and/or objectidentification processes as described above may then be compared tocoordinates predicted using initial guess at transformation matrices; anerror function may be computed using by comparing the two sets ofpoints, and new x, y, and/or z coordinates, may be iteratively estimatedand compared until the error function drops below a threshold level. Insome cases, a machine vision system may use a classifier, such as anyclassifier described throughout this disclosure.

With continued reference to FIG. 1 , the sensor 144 may include avariety of sensors. For example, sensor 144 may be a scale. As anon-limiting example, sensor 144 may detect the weight of the medicationdonation and transmit this information to computing device 104 as actualmedication information 116. In some embodiments, sensor 144 may includea barcode reader. A “barcode reader,” for the purposes of thisdisclosure, includes a light source, and a light sensor configured todetect optical impulses reflected back from the light source off of thebarcode being scanned. The barcode may be consistent with any barcodedisclosed in this disclosure. A barcode reader may be configured to reada barcode, such as barcode 404 in FIG. 4 , and transmit this informationto computing device 104 as actual medication information 116. Sensor 144may be part of a sensor suite. For example, sensor 144 may include aplurality of sensing devices, such as a camera, a barcode scanner,and/or a scale. A person of ordinary skill in the art would appreciatethat a variety of different types of sensors may be included in thesensor suite depending on the exact functionality needed.

Referring now to FIG. 2 , FIG. 2 depicts an exemplary embodiment of acomputing device 104. Computing device 104 may include a machinelearning module 200. Machine learning module may be consistent withmachine learning module 1000 discussed with reference to FIG. 10 .

Still referring to FIG. 2 , computing device 104 may include an imageclassifier 204. Computing device 104 may be configured to perform imageclassification using image classifier 204. Image classifier 204 may becommunicatively connected to computing device 104. In some embodiments,image classifier 204 may be a component or module of computing device104. A “classifier,” as used in this disclosure is a machine-learningmodel, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Computing device 104 and/or anothercomputing device may generate a classifier using a classificationalgorithm, defined as a process whereby a computing device derives aclassifier from training data. Classification may be performed using,without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, Fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, kernel estimation, learning vectorquantization, and/or neural network-based classifiers.

Still referring to FIG. 2 , image classifier 204 may be generated, as anon-limiting example, using a Naïve Bayes classification algorithm.Naïve Bayes classification algorithm generates classifiers by assigningclass labels to problem instances, represented as vectors of elementvalues. Class labels are drawn from a finite set. Naïve Bayesclassification algorithm may include generating a family of algorithmsthat assume that the value of a particular element is independent of thevalue of any other element, given a class variable. Naïve Bayesclassification algorithm may be based on Bayes Theorem expressed asP(A/B)=P(B/A)P(A)÷P(B), where P(A/B) is the probability of hypothesis Agiven data B also known as posterior probability; P(B/A) is theprobability of data B given that the hypothesis A was true; P(A) is theprobability of hypothesis A being true regardless of data also known asprior probability of A; and P(B) is the probability of the dataregardless of the hypothesis. A naïve Bayes algorithm may be generatedby first transforming training data into a frequency table. A computingdevice may then calculate a likelihood table by calculatingprobabilities of different data entries and classification labels. Acomputing device may utilize a naïve Bayes equation to calculate aposterior probability for each class. A class containing the highestposterior probability is the outcome of prediction. Naïve Bayesclassification algorithm may include a gaussian model that follows anormal distribution. Naïve Bayes classification algorithm may include amultinomial model that is used for discrete counts. Naïve Bayesclassification algorithm may include a Bernoulli model that may beutilized when vectors are binary.

With continued reference to FIG. 2 , image classifier 204 may begenerated using a K-nearest neighbors (KNN) algorithm. A “K-nearestneighbors algorithm” as used in this disclosure, includes aclassification method that utilizes feature similarity to analyze howclosely out-of-sample-features resemble training data to classify inputdata to one or more clusters and/or categories of features asrepresented in training data; this may be performed by representing bothtraining data and input data in vector forms, and using one or moremeasures of vector similarity to identify classifications withintraining data, and to determine a classification of input data.K-nearest neighbors algorithm may include specifying a K-value, or anumber directing the classifier to select the k most similar entriestraining data to a given sample, determining the most common classifierof the entries in the database, and classifying the known sample; thismay be performed recursively and/or iteratively to generate a classifierthat may be used to classify input data as further samples. Forinstance, an initial set of samples may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship, whichmay be seeded, without limitation, using expert input received accordingto any process as described herein. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data. Heuristic may include selecting somenumber of highest-ranking associations and/or training data elements.

With continued reference to FIG. 2 , generating k-nearest neighborsalgorithm may generate a first vector output containing a data entrycluster, generating a second vector output containing an input data, andcalculate the distance between the first vector output and the secondvector output using any suitable norm such as cosine similarity,Euclidean distance measurement, or the like. Each vector output may berepresented, without limitation, as an n-tuple of values, where n is atleast two values. Each value of n-tuple of values may represent ameasurement or other quantitative value associated with a given categoryof data, or attribute, examples of which are provided in further detailbelow; a vector may be represented, without limitation, in n-dimensionalspace using an axis per category of value represented in n-tuple ofvalues, such that a vector has a geometric direction characterizing therelative quantities of attributes in the n-tuple as compared to eachother. Two vectors may be considered equivalent where their directions,and/or the relative quantities of values within each vector as comparedto each other, are the same; thus, as a non-limiting example, a vectorrepresented as [5, 10, 15] may be treated as equivalent, for purposes ofthis disclosure, as a vector represented as [1, 2, 3]. Vectors may bemore similar where their directions are more similar, and more differentwhere their directions are more divergent; however, vector similaritymay alternatively or additionally be determined using averages ofsimilarities between like attributes, or any other measure of similaritysuitable for any n-tuple of values, or aggregation of numericalsimilarity measures for the purposes of loss functions as described infurther detail below. Any vectors as described herein may be scaled,such that each vector represents each attribute along an equivalentscale of values. Each vector may be “normalized,” or divided by a“length” attribute, such as a length attribute l as derived using aPythagorean norm: l=√{square root over (Σ_(i=0) ^(n)a_(i) ²)}, wherea_(i) is attribute number i of the vector. Scaling and/or normalizationmay function to make vector comparison independent of absolutequantities of attributes, while preserving any dependency on similarityof attributes; this may, for instance, be advantageous where casesrepresented in training data are represented by different quantities ofsamples, which may result in proportionally equivalent vectors withdivergent values.

Still referring to FIG. 2 , in some embodiments, computing device 104may be configured to train image classifier 204 or any machine learningmodule (e.g., machine learning module 1000 in FIG. 10 or machinelearning module 200) using any classification algorithm described aboveoperating on training data. “Training data,” as used herein, is datacontaining correlations that a machine-learning process, such as aclassifier, may use to model relationships between two or morecategories of data elements. For instance, and without limitation,training data may include a plurality of data entries, each entryrepresenting a set of data elements that were recorded, received, and/orgenerated together; data elements may be correlated by shared existencein a given data entry, by proximity in a given data entry, or the like.Multiple data entries in training data may evince one or more trends incorrelations between categories of data elements; for instance, andwithout limitation, a higher value of a first data element belonging toa first category of data element may tend to correlate to a higher valueof a second data element belonging to a second category of data element,indicating a possible proportional or other mathematical relationshiplinking values belonging to the two categories. Multiple categories ofdata elements may be related in training data according to variouscorrelations; correlations may indicate causative and/or predictivelinks between categories of data elements, which may be modeled asrelationships such as mathematical relationships by machine-learningprocesses as described in further detail below. Training data may beformatted and/or organized by categories of data elements, for instanceby associating data elements with one or more descriptors correspondingto categories of data elements. As a non-limiting example, training datamay include data entered in standardized forms by persons or processes,such that entry of a given data element in a given field in a form maybe mapped to one or more descriptors of categories. Elements in trainingdata may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training datamay be provided in fixed-length formats, formats linking positions ofdata to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively, or additionally, and further referring to FIG. 2 ,training data may include one or more elements that are not categorized;that is, training data may not be formatted or contain descriptors forsome elements of data. Machine-learning algorithms and/or otherprocesses may sort training data according to one or morecategorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. Training data used to train image classifier 204may include a plurality of entries, each including attributes of animage such as a portion of a frame of a plurality of frames, and/or ashape detected therein, which may be used to classify the image to otherimages in training data.

With continued reference to FIG. 2 , computing device 104 may include anOCR module 208. Still refereeing to FIG. 2 , in some embodiments,optical character recognition or optical character reader (OCR) includesautomatic conversion of images of written (e.g., typed, handwritten orprinted text) into machine-encoded text. In some cases, recognition ofat least a keyword from an image component may include one or moreprocesses, including without limitation optical character recognition(OCR), optical word recognition, intelligent character recognition,intelligent word recognition, and the like. In some cases, OCR mayrecognize written text, one glyph or character at a time. In some cases,optical word recognition may recognize written text, one word at a time,for example, for languages that use a space as a word divider. In somecases, intelligent character recognition (ICR) may recognize writtentext one glyph or character at a time, for instance by employing machinelearning processes. In some cases, intelligent word recognition (IWR)may recognize written text, one word at a time, for instance byemploying machine learning processes.

Still referring to FIG. 2 , in some cases OCR may be an “offline”process, which analyses a static document or image frame. In some cases,handwriting movement analysis can be used as input to handwritingrecognition. For example, instead of merely using shapes of glyphs andwords, this technique may capture motions, such as the order in whichsegments are drawn, the direction, and the pattern of putting the pendown and lifting it. This additional information can make handwritingrecognition more accurate. In some cases, this technology may bereferred to as “online” character recognition, dynamic characterrecognition, real-time character recognition, and intelligent characterrecognition.

Still referring to FIG. 2 , in some cases, OCR processes may employpre-processing of image component. Pre-processing process may includewithout limitation de-skew, de-speckle, binarization, line removal,layout analysis or “zoning,” line and word detection, scriptrecognition, character isolation or “segmentation,” and normalization.In some cases, a de-skew process may include applying a transform (e.g.,homography or affine transform) to image component to align text. Insome cases, a de-speckle process may include removing positive andnegative spots and/or smoothing edges. In some cases, a binarizationprocess may include converting an image from color or greyscale toblack-and-white (i.e., a binary image). Binarization may be performed asa simple way of separating text (or any other desired image component)from a background of image component. In some cases, binarization may berequired for example if an employed OCR algorithm only works on binaryimages. In some cases. a line removal process may include removal ofnon-glyph or non-character imagery (e.g., boxes and lines). In somecases, a layout analysis or “zoning” process may identify columns,paragraphs, captions, and the like as distinct blocks. In some cases, aline and word detection process may establish a baseline for word andcharacter shapes and separate words, if necessary. In some cases, ascript recognition process may, for example in multilingual documents,identify script allowing an appropriate OCR algorithm to be selected. Insome cases, a character isolation or “segmentation” process may separatesignal characters, for example character-based OCR algorithms. In somecases, a normalization process may normalize aspect ratio and/or scaleof image component.

Still referring to FIG. 2 , in some embodiments an OCR process willinclude an OCR algorithm. Exemplary OCR algorithms include matrixmatching process and/or feature extraction processes. Matrix matchingmay involve comparing an image to a stored glyph on a pixel-by-pixelbasis. In some cases, matrix matching may also be known as “patternmatching,” “pattern recognition,” and/or “image correlation.” Matrixmatching may rely on an input glyph being correctly isolated from therest of the image component. Matrix matching may also rely on a storedglyph being in a similar font and at the same scale as input glyph.Matrix matching may work best with typewritten text.

Still referring to FIG. 2 , in some embodiments, an OCR process mayinclude a feature extraction process. In some cases, feature extractionmay decompose a glyph into features. Exemplary non-limiting features mayinclude corners, edges, lines, closed loops, line direction, lineintersections, and the like. In some cases, feature extraction mayreduce dimensionality of representation and may make the recognitionprocess computationally more efficient. In some cases, extractedfeatures can be compared with an abstract vector-like representation ofa character, which might reduce to one or more glyph prototypes. Generaltechniques of feature detection in computer vision are applicable tothis type of OCR. In some embodiments, machine-learning processes likenearest neighbor classifiers (e.g., k-nearest neighbors algorithm) canbe used to compare image features with stored glyph features and choosea nearest match. OCR may employ any machine-learning process describedin this disclosure, for example machine-learning processes describedwith reference to FIGS. 1, 2, and 8-10 . Exemplary non-limiting OCRsoftware includes Cuneiform and Tesseract. Cuneiform is amulti-language, open-source optical character recognition systemoriginally developed by Cognitive Technologies of Moscow, Russia.Tesseract is free OCR software originally developed by Hewlett-Packardof Palo Alto, Calif., United States.

Still referring to FIG. 2 , in some cases, OCR may employ a two-passapproach to character recognition. Second pass may include adaptiverecognition and use letter shapes recognized with high confidence on afirst pass to recognize better remaining letters on the second pass. Insome cases, two-pass approach may be advantageous for unusual fonts orlow-quality image components where visual verbal content may bedistorted. Another exemplary OCR software tool include OCRopus. OCRopusdevelopment is led by German Research Centre for Artificial Intelligencein Kaiserslautern, Germany. In some cases, OCR software may employneural networks, for example neural networks as taught in reference toFIGS. 8 and 9 .

Still referring to FIG. 2 , in some cases, OCR may includepost-processing. For example, OCR accuracy can be increased, in somecases, if output is constrained by a lexicon. A lexicon may include alist or set of words that are allowed to occur in a document. In somecases, a lexicon may include, for instance, all the words in the Englishlanguage, or a more technical lexicon for a specific field. In somecases, an output stream may be a plain text stream or file ofcharacters. In some cases, an OCR process may preserve an originallayout of visual verbal content. In some cases, near-neighbor analysiscan make use of co-occurrence frequencies to correct errors, by notingthat certain words are often seen together. For example, “Washington,D.C.” is generally far more common in English than “Washington DOC.” Insome cases, an OCR process may make us of a priori knowledge of grammarfor a language being recognized. For example, grammar rules may be usedto help determine if a word is likely to be a verb or a noun. Distanceconceptualization may be employed for recognition and classification.For example, a Levenshtein distance algorithm may be used in OCRpost-processing to further optimize results.

Now referring to FIG. 3 , a flow chart for an exemplary method 300 forverifying the medication donation is shown. In some embodiments, method300 may include any of the medication donation verification methodsdisclosed with reference to FIG. 1 . Method 300 may include step 305 ofverifying the donated medication using product identifiers. For thepurposes of this disclosure, a “product identifier” is a piece ofinformation that identifies the medication contained in a medicationdonation. Product identifier may include product codes, product names,trademarks, brand names, serial numbers, identification numbers,barcodes, imprints and the like. In some embodiments, step 305 may becarried out manually by a person or team of people. In some embodiments,step 305 may be carried out at least in part by using a machine learningmodel, such as identification machine learning model disclosed in thisdisclosure. In some embodiments, step 305 may include the use of OCR, oran image classifier as disclosed in this disclosure.

With continued reference to FIG. 3 , method 300 may further include step310 of inspection of the medication donation by a pharmacist. Thepharmacist may be consistent with any other pharmacist disclosed as partof this disclosure. In some embodiments, the pharmacist could be aregistered pharmacist (RPh). As a non-limiting example, the pharmacistmay inspect each pill in medication donation, including by visualinspection, verify the drug identity of the medicine in medicationdonation, verify the dosage of the medicine in medication donation,verify the drug expiration date, and/or certify the intact tamperevident seals.

With continued reference to FIG. 3 , method 300 may further include astep 315 of verifying the medication donation using chemical analysis.In some embodiments, this step 315 may be optional. Particularly, invarious embodiments, step 315 may be performed for a random selection ofmedication donation. As a non-limiting example, this may be accomplishedusing the output of a random number generator. By changing what outputfrom the random number generator causes the medication donation toundergo further verification testing, the probability of a givenmedication donation being selected can be adjusted. In some embodiments,step 315 may be performed for every n^(th) medication donation, whereinn is an integer above zero. As a non-limiting example, where n=100 every100^(th) medication donation may be selected to undergo step 315. Insome embodiments, a statistically relevant sample of medicationdonations may be selected to undergo step 315. In some embodiments, step315 may include sending the medication donation to a third-partychemical analytic company. The chemical analysis of step 315 may includeany relevant chemical analysis technique known in the art. As anon-limiting example, the chemical analysis of step 315 may includeperforming an assay. Without limitation, the assay may include liquidchromatography-mass spectrometry (LC-MS), liquidchromatography-ultraviolet spectrometry (LC-UV), or the like. In someembodiments, the results of the assays may be compared against qualityassurance information, such as United States Pharmacopeia (USP)monographs. As a non-limiting example, the chemical analysis may includespectroscopy. As another non-limiting example, the chemical analysis mayinclude mass spectroscopy. As another non-limiting example, the chemicalanalysis may include thermal analysis. In some embodiments, method 300may be a proprietary three-step medication verification process.

Referring now to FIG. 4 , exemplary packaging 400 for a medicationdonation is shown. While exemplary packaging 400 is depicted in FIG. 4as a bottle, in various embodiments, exemplary packaging 400 may includea syringe, bag, box, container, blister pack, and the like. In someembodiments, exemplary packaging 400 may include a vial. In someembodiments, exemplary packaging 400 may include a unit-of-use package,wherein a unit-of-use package contains prescription medication in aquantity designed to be dispensed directly to a patient. In someembodiments, exemplary packaging 400 may include a unit-dose, wherein aunit-dose is an amount of medication indented to be administered to apatient in a single dose. In some embodiments, exemplary packaging 400may include, as an example, a barcode 404. A “barcode,” for the purposesof this disclosure is a non-textual pattern used to represent data in amachine-readable form. As a non-limiting example, barcode 404 mayinclude a GS1 barcode, a QR code, a UPC code, an EAN code, a datamatrix, and the like.

Continuing to refer to FIG. 4 , exemplary packaging 400 may include aset of medication information 408. Medication information 408 maycontain a variety of information regarding a medication donation.Medication information 408 may include, as a non-limiting example, thegeneric name for the medicine in medication donation, the quantity ofmedication in medication donation, the dosage of the medicine inmedication donation, the source of the medicine in medication donation,the expiration date of the medicine in the medication donation, and thelike. Medication information 408 may be presented in any logical format,including, as non-limiting examples, a table, a list, a matrix, and thelike.

With continued reference to FIG. 4 , actual medication information, asdisclosed in this disclosure, may be collected, in some embodiments,from exemplary packaging. In some embodiment, this may be done using OCRas disclosed in this disclosure. In some embodiments, an imageclassifier may be used to collect actual medication information. In someembodiments, a machine learning model may be used to sort and identifythe actual medication information from, for example, barcode 404 andmedication information 408. In some embodiments, a machine visionsystem, such as the one described with reference to FIG. 1 , may be usedto process an image of any portion of exemplary packaging 400 toidentify information from the image. For example, the machine visionsystem could identify barcode 404 or medication information 408. As anon-limiting example, the machine vision system could count the numberof pills in a package.

With continued reference to FIG. 4 , in some embodiments, actualmedication information may be collected from an interior packaging ofthe medication donation. For the purposes of this disclosure, an“interior packaging” is a piece of packaging that is concealed behind anexterior packaging when the exterior packaging is intact. For thepurposes of this disclosure, an “exterior packaging” is the outermostpackaging of a medication donation. Interior packaging may includemedication information 408 and/or barcode 404 in a similar manner toexemplary packaging 400. For example, in some cases a blister pack mayinclude medication information 408 and/or barcode 404 printed on, forexample, its backside. In other cases, a medication donation comprisingpills, capsules, or the like may be broken into individual packets ofpills, capsules, or the like. In some embodiments, these individualpackets may contain a single dose of medication. In some embodiments,these individual packets, which are interior packaging, may includemedication information 408 and/or barcode 404 printed on them.

Referring now to FIG. 5 , a first medication pill 500 and a secondmedication pill 504 are depicted. First medication pill 500 may have adifferent size or shape from second medication pill 504. In someembodiments, first medication pill 500 may have, for example, adifferent coating or texture from second medication pill 504; this isindicated by the different fill patterns in FIG. 5 . In someembodiments, first medication pill 500 and/or second medication pill 504may include unique identifiers, wherein the unique identifiers arealphanumeric strings imprinted or printed on first medication pill 500and/or second medication pill 504. In some embodiments, an imageclassifier may be used to collect actual medication information from afirst medication pill 500 and/or second medication pill 504, forexample, using a picture of first medication pill 500. In someembodiments, a machine learning model may be used to collect actualmedication information from first medication pill 500. The machinelearning model may be trained on pictures or videos of variousmedication pills and the medication information corresponding to thosemedication pills. Using these techniques, first medication pill 500 canbe differentiated from second medication pill 504 when collecting theactual medication information. In some embodiments, a machine visionsystem, such as the one described with reference to FIG. 1 , may be usedto process an image of first medication pill 500 and/or secondmedication pill 504 to identify information from the image. For example,the machine vision system could identify the size, shape, coating,texture, and/or unique identifier from first medication pill 500 and/orsecond medication pill 504.

With continued reference to FIG. 5 , in some embodiments, firstmedication pill 500 and/or second medication pill 504 may have variousblemishes. The blemishes may include cracks, dents, chips, and the like.The blemishes may be the result of tampering. In some embodiments, animage classifier may be used to detect blemishes on first medicationpill 500 and/or second medication pill 504. As a non-limiting example,this may be done using a stock image of first medication pill 500 and/orsecond medication pill 504. In some embodiments, a machine visionsystem, such as the one described with reference to FIG. 1 may beconfigured to detect blemishes on first medication pill 500 and/orsecond medication pill 504. As a non-limiting example, machine visionsystem may detect a discontinuity in the shape of pill 500/504,suggesting a blemish.

With continued reference to FIG. 5 , in some embodiments, there may be aplurality of the same medication pill, such as, as non-limitingexamples, first medication pill 500 and second medication pill 504. Insome embodiments, a machine vision system, such as the one describedabove with respect to FIG. 1 , may be used to detect the uniformity ofthe plurality of the same medication pill. As non-limiting examples, themachine vision system may detect variations in size, length, color,thickness, shape, texture, and the like. Non-uniformity among theplurality of the same medication pill may be considered to indicatetampering.

Referring now to FIG. 6 , a method for redistributing medication 600 isshown. In some embodiments, method 600 may be performed by a computingdevice (e.g., computing device 104 discussed with reference to FIG. 1 ).Method 600 includes step 605 of receiving a communication regarding amedication donation from a donor, wherein the communication includesdonor medication information regarding the medication donation. Thecommunication may be consistent with communication 108 discussed withreference to FIG. 1 . Medication donation may be consistent with anymedication donation disclosed as part of this disclosure. Donormedication information may be consistent with any donor medicationinformation disclosed as part of this disclosure. Donor may beconsistent with any donor disclosed as part of this disclosure. In someembodiments, a communication regarding a medication donation from adonor may comprise automatically generating a shipping label for themedication donation. The process of automatically generating a shippinglabel may be consistent with any process for automatically generating ashipping label disclosed in this disclosure. The shipping label may beconsistent with any shipping label disclosed as part of this disclosure.

With continued reference to FIG. 6 , method 600 further includes step610 of verifying the medication donation, wherein verifying themedication donation includes verifying the identity of the medicationdonation. Verifying the medication donation may be consistent with anyprocess for verifying the medication donation disclosed as part of thisdisclosure. Verifying the identity of the medication donation may beconsistent with any process for verifying the identity of the medicationdonation disclosed as part of this disclosure. In some embodiments, step610 may further include verifying the integrity of the medicationdonation. Verifying the integrity of the medication donation may beconsistent with any process for verifying the integrity of themedication donation disclosed as part of this disclosure. In someembodiments, step 610 may further include selecting, optionally, themedication donation for further donation. Selecting, optionally, themedication donation for further verification may be consistent with anyprocess for selecting, optionally, the medication donation for furtherverification disclosed as part of this disclosure. In some embodiments,step 610 may further include creating a verification record including averification status of the medication donation, wherein the verificationrecord is stored using an immutable sequential listing. The verificationrecord may be consistent with any verification record disclosed as partof this disclosure. The verification status of the medication donationmay be consistent with any verification status of the medicationdonation disclosed as part of this disclosure. The immutable sequentiallisting may be consistent with any immutable sequential listingdisclosed as part of this disclosure. Verifying the identity of themedication donation includes collecting actual medication informationfrom the medication donation. The process of collecting actualmedication information from the medication donation may be consistentwith any process for collecting actual medication information disclosedas part of this disclosure. Actual medication information may beconsistent with any actual medication information disclosed as part ofthis disclosure. Verifying the identity of the medication donation mayalso include using an identification machine learning model to verifythe identity of the medication donation, wherein the identificationmachine learning model may be trained to verify the identity of themedication donation by comparing the donor medication information to theactual medication information. Identification machine learning model maybe consistent with any identification machine learning model disclosedas part of this disclosure. In some embodiments, the step of collectingactual medication information may include using a sensor to collectactual medication information. The sensor may be consistent with anysensor disclosed as part of this disclosure; particularly sensor 144 inFIG. 1 . In some embodiments, the sensor may include a camera. Thecamera may be consistent with any camera disclosed as part of thisdisclosure. In some embodiments, collecting actual medicationinformation may include receiving an image of the medication donationfrom the camera and using an image classifier to classify the image. Theimage classifier may be consistent with any image classifier disclosedas part of this disclosure.

With continued reference to FIG. 6 , method 600 further includes step615 of entering final medication information corresponding to themedication donation into the medication database. Final medicationinformation may be consistent with any final medication informationdisclosed as part of this disclosure. Medication database may beconsistent with any medication database disclosed as part of thisdisclosure.

With continued reference to FIG. 6 , method 600 further includes a step620 of matching the medication donation to a recipient selected from arecipient database as a function of a set of recipient data, whereineach recipient in the recipient database has an associated set ofrecipient data. The recipient may be consistent with any recipientdisclosed as part of this disclosure. The recipient database may beconsistent with any recipient database disclosed as part of thisdisclosure. Recipient data may be consistent with any recipient datadisclosed as part of this disclosure. In some embodiments, step 620 mayfurther include using a matching machine learning model to match themedication donation to a recipient from the recipient database, whereinthe matching machine learning model that takes the final medicationinformation from the medication database as input and outputs at least arecipient from the recipient database. Matching machine learning modelmay be consistent with any matching machine learning model disclosed aspart of this disclosure.

With continued reference to FIG. 6 , method 600 may further includeremoving the medication donation information corresponding to amedication donation from the medication database when a buffer timeperiod before an expiration date of the medication donation is reached.The buffer time period may be consistent with any buffer time perioddisclosed as part of this disclosure. The expiration data of themedication donation may be consistent with any expiration data of themedication donation disclosed as part of this disclosure. In someembodiments, method 600 may further include verifying the identity ofthe donor using at least a donor credential from the donor. The processof verifying the identity of the donor may be consistent with anyprocess of verifying the identity of the donor disclosed as part of thisdisclosure. The donor credential may be consistent with donor credential136 discussed with reference to FIG. 1 . In some embodiments, method 600may further include identifying a central repository to store themedical donation. The process for identifying a central repository tostore the medical donation may be consistent with any process foridentifying a central repository to store the medical donation disclosedas part of this disclosure. Central repository may be consistent withany central repository disclosed as part of this disclosure.

With continued reference to FIG. 6 , in some embodiments, method 600 mayinclude creating a verification record comprising a verification statusfor the medication donation and a tampering status for the medicationdonation. In some embodiments, method 600 may include storing theverification record on an immutable sequential listing. This may beimplemented in as disclosed with reference to FIGS. 1-5 and 7-10 .

With continued reference to FIG. 6 , in some embodiments, step 610 mayinclude collecting the actual medication information for the medicationdonation using a sensor, wherein the sensor comprises a camera. In someembodiments, collecting the actual medication information may includereceiving a first image of an exterior packaging the medication donationfrom the camera. In some embodiments, collecting the actual medicationinformation may include receiving a second image of an interiorpackaging of the medication donation from the camera. In someembodiments, collecting the actual medication information may includecollecting a first set of medication information from the first image.In some embodiments, collecting the actual medication information mayinclude collecting a second set of medication information from thesecond image. In some embodiments, the interior packaging may include ablister pack. This may be implemented in as disclosed with reference toFIGS. 1-5 and 7-10 .

With continued reference to FIG. 6 , step 610 of verifying themedication donation may include checking the medication donation fortampering. In some embodiments, checking the medication donation fortampering may include determining a tampering status. In someembodiments, checking the medication donation for tampering may includecomparing the first set of medication information to the second set ofmedication information. This may be implemented in as disclosed withreference to FIGS. 1-5 and 7-10 .

Referring now to FIG. 7 , an exemplary embodiment of an immutablesequential listing 700 is illustrated. Data elements are listed inimmutable sequential listing 700; data elements may include any form ofdata, including textual data, image data, encrypted data,cryptographically hashed data, and the like. Data elements may include,without limitation, one or more at least a digitally signed assertion.In one embodiment, a digitally signed assertion 704 is a collection oftextual data signed using a secure proof as described in further detailbelow; secure proof may include, without limitation, a digital signatureas described above. Collection of textual data may contain any textualdata, including without limitation American Standard Code forInformation Interchange (ASCII), Unicode, or similar computer-encodedtextual data, any alphanumeric data, punctuation, diacritical mark, orany character or other marking used in any writing system to conveyinformation, in any form, including any plaintext or cyphertext data; inan embodiment, collection of textual data may be encrypted, or may be ahash of other data, such as a root or node of a Merkle tree or hashtree, or a hash of any other information desired to be recorded in somefashion using a digitally signed assertion 204. In an embodiment,collection of textual data states that the owner of a certaintransferable item represented in a digitally signed assertion 204register is transferring that item to the owner of an address. Adigitally signed assertion 204 may be signed by a digital signaturecreated using the private key associated with the owner's public key, asdescribed above.

Still referring to FIG. 7 , a digitally signed assertion 704 maydescribe a transfer of virtual currency, such as crypto currency asdescribed below. The virtual currency may be a digital currency. Item ofvalue may be a transfer of trust, for instance represented by astatement vouching for the identity or trustworthiness of the firstentity. Item of value may be an interest in a fungible negotiablefinancial instrument representing ownership in a public or privatecorporation, a creditor relationship with a governmental body or acorporation, rights to ownership represented by an option, derivativefinancial instrument, commodity, debt-backed security such as a bond ordebenture or other security as described in further detail below. Aresource may be a physical machine e.g., a ride share vehicle or anyother asset. A digitally signed assertion 704 may describe the transferof a physical good; for instance, a digitally signed assertion 704 maydescribe the sale of a product. In some embodiments, a transfernominally of one item may be used to represent a transfer of anotheritem; for instance, a transfer of virtual currency may be interpreted asrepresenting a transfer of an access right; conversely, where the itemnominally transferred is something other than virtual currency, thetransfer itself may still be treated as a transfer of virtual currency,having value that depends on many potential factors including the valueof the item nominally transferred and the monetary value attendant tohaving the output of the transfer moved into a particular user'scontrol. The item of value may be associated with a digitally signedassertion 204 by means of an exterior protocol, such as the COLOREDCOINS created according to protocols developed by The Colored CoinsFoundation, the MASTERCOIN protocol developed by the MastercoinFoundation, or the ETHEREUM platform offered by the Stiftung EthereumFoundation of Baar, Switzerland, the Thunder protocol developed byThunder Consensus, or any other protocol.

Still referring to FIG. 7 , in one embodiment, an address is a textualdatum identifying the recipient of virtual currency or another item ofvalue in a digitally signed assertion 704. In some embodiments, theaddress is linked to a public key, the corresponding private key ofwhich is owned by the recipient of a digitally signed assertion 704. Forinstance, address may be the public key. Address may be arepresentation, such as a hash, of the public key. Address may be linkedto the public key in memory of a computing device, for instance via a“wallet shortener” protocol. Where address is linked to a public key, atransferee in a digitally signed assertion 704 may record a subsequent adigitally signed assertion 704 transferring some or all of the valuetransferred in the first a digitally signed assertion 704 to a newaddress in the same manner. A digitally signed assertion 704 may containtextual information that is not a transfer of some item of value inaddition to, or as an alternative to, such a transfer. For instance, asdescribed in further detail below, a digitally signed assertion 704 mayindicate a confidence level associated with a distributed storage nodeas described in further detail below.

In an embodiment, and still referring to FIG. 7 immutable sequentiallisting 700 records a series of at least a posted content in a way thatpreserves the order in which the at least a posted content took place.Temporally sequential listing may be accessible at any of varioussecurity settings; for instance, and without limitation, temporallysequential listing may be readable and modifiable publicly, may bepublicly readable but writable only by entities and/or devices havingaccess privileges established by password protection, confidence level,or any device authentication procedure or facilities described in thisdisclosure, or may be readable and/or writable only by entities and/ordevices having such access privileges. Access privileges may exist inmore than one level, including, without limitation, a first access levelor community of permitted entities and/or devices having ability toread, and a second access level or community of permitted entitiesand/or devices having ability to write; first and second community maybe overlapping or non-overlapping. In an embodiment, posted contentand/or immutable sequential listing 700 may be stored as one or morezero knowledge sets (ZKS), Private Information Retrieval (PIR)structure, or any other structure that allows checking of membership ina set by querying with specific properties. Such a database mayincorporate protective measures to ensure that malicious actors may notquery the database repeatedly in an effort to narrow the members of aset to reveal uniquely identifying information of a given postedcontent.

Still referring to FIG. 7 , immutable sequential listing 700 maypreserve the order in which the at least a posted content took place bylisting them in chronological order; alternatively or additionally,immutable sequential listing 700 may organize digitally signedassertions 704 into sub-listings 708 such as “blocks” in a blockchain,which may be themselves collected in a temporally sequential order;digitally signed assertions 704 within a sub-listing 708 may or may notbe temporally sequential. The ledger may preserve the order in which atleast a posted content took place by listing them in sub-listings 708and placing the sub-listings 708 in chronological order. The immutablesequential listing 700 may be a distributed, consensus-based ledger,such as those operated according to the protocols promulgated by RippleLabs, Inc., of San Francisco, Calif., or the Stellar DevelopmentFoundation, of San Francisco, Calif., or of Thunder Consensus. In someembodiments, the ledger is a secured ledger; in one embodiment, asecured ledger is a ledger having safeguards against alteration byunauthorized parties. The ledger may be maintained by a proprietor, suchas a system administrator on a server, that controls access to theledger; for instance, the user account controls may allow contributorsto the ledger to add at least a posted content to the ledger but may notallow any users to alter at least a posted content that have been addedto the ledger. In some embodiments, ledger is cryptographically secured;in one embodiment, a ledger is cryptographically secured where each linkin the chain contains encrypted or hashed information that makes itpractically infeasible to alter the ledger without betraying thatalteration has taken place, for instance by requiring that anadministrator or other party sign new additions to the chain with adigital signature. Immutable sequential listing 700 may be incorporatedin, stored in, or incorporate, any suitable data structure, includingwithout limitation any database, datastore, file structure, distributedhash table, directed acyclic graph or the like. In some embodiments, thetimestamp of an entry is cryptographically secured and validated viatrusted time, either directly on the chain or indirectly by utilizing aseparate chain. In one embodiment the validity of timestamp is providedusing a time stamping authority as described in the RFC 3161 standardfor trusted timestamps, or in the ANSI ASC x9.95 standard. In anotherembodiment, the trusted time ordering is provided by a group of entitiescollectively acting as the time stamping authority with a requirementthat a threshold number of the group of authorities sign the timestamp.

In some embodiments, and with continued reference to FIG. 7 , immutablesequential listing 700, once formed, may be inalterable by any party, nomatter what access rights that party possesses. For instance, immutablesequential listing 700 may include a hash chain, in which data is addedduring a successive hashing process to ensure non-repudiation. Immutablesequential listing 700 may include a block chain. In one embodiment, ablock chain is immutable sequential listing 700 that records one or morenew at least a posted content in a data item known as a sub-listing 708or “block.” An example of a block chain is the BITCOIN block chain usedto record BITCOIN transactions and values. Sub-listings 708 may becreated in a way that places the sub-listings 708 in chronological orderand link each sub-listing 708 to a previous sub-listing 708 in thechronological order so that any computing device may traverse thesub-listings 708 in reverse chronological order to verify any at least aposted content listed in the block chain. Each new sub-listing 708 maybe required to contain a cryptographic hash describing the previoussub-listing 708. In some embodiments, the block chain contains a singlefirst sub-listing 708 sometimes known as a “genesis block.”

Still referring to FIG. 7 , the creation of a new sub-listing 708 may becomputationally expensive; for instance, the creation of a newsub-listing 708 may be designed by a “proof of work” protocol acceptedby all participants in forming the immutable sequential listing 700 totake a powerful set of computing devices a certain period of time toproduce. Where one sub-listing 708 takes less time for a given set ofcomputing devices to produce the sub-listing 708 protocol may adjust thealgorithm to produce the next sub-listing 708 so that it will requiremore steps; where one sub-listing 708 takes more time for a given set ofcomputing devices to produce the sub-listing 708 protocol may adjust thealgorithm to produce the next sub-listing 708 so that it will requirefewer steps. As an example, protocol may require a new sub-listing 708to contain a cryptographic hash describing its contents; thecryptographic hash may be required to satisfy a mathematical condition,achieved by having the sub-listing 708 contain a number, called a nonce,whose value is determined after the fact by the discovery of the hashthat satisfies the mathematical condition. Continuing the example, theprotocol may be able to adjust the mathematical condition so that thediscovery of the hash describing a sub-listing 708 and satisfying themathematical condition requires more or less steps, depending on theoutcome of the previous hashing attempt. Mathematical condition, as anexample, might be that the hash contains a certain number of leadingzeros and a hashing algorithm that requires more steps to find a hashcontaining a greater number of leading zeros, and fewer steps to find ahash containing a lesser number of leading zeros. In some embodiments,production of a new sub-listing 708 according to the protocol is knownas “mining.” The creation of a new sub-listing 708 may be designed by a“proof of stake” protocol as will be apparent to those skilled in theart upon reviewing the entirety of this disclosure.

Continuing to refer to FIG. 7 , in some embodiments, protocol alsocreates an incentive to mine new sub-listings 708. The incentive may befinancial; for instance, successfully mining a new sub-listing 708 mayresult in the person or entity that mines the sub-listing 708 receivinga predetermined amount of currency. The currency may be fiat currency.Currency may be cryptocurrency as defined below. In other embodiments,incentive may be redeemed for particular products or services; theincentive may be a gift certificate with a particular business, forinstance. In some embodiments, incentive is sufficiently attractive tocause participants to compete for the incentive by trying to race eachother to the creation of sub-listings 708 Each sub-listing 708 createdin immutable sequential listing 700 may contain a record or at least aposted content describing one or more addresses that receive anincentive, such as virtual currency, as the result of successfullymining the sub-listing 708.

With continued reference to FIG. 7 , where two entities simultaneouslycreate new sub-listings 708, immutable sequential listing 700 maydevelop a fork; protocol may determine which of the two alternatebranches in the fork is the valid new portion of the immutablesequential listing 700 by evaluating, after a certain amount of time haspassed, which branch is longer. “Length” may be measured according tothe number of sub-listings 708 in the branch. Length may be measuredaccording to the total computational cost of producing the branch.Protocol may treat only at least a posted content contained the validbranch as valid at least a posted content. When a branch is foundinvalid according to this protocol, at least a posted content registeredin that branch may be recreated in a new sub-listing 708 in the validbranch; the protocol may reject “double spending” at least a postedcontent that transfer the same virtual currency that another at least aposted content in the valid branch has already transferred. As a result,in some embodiments the creation of fraudulent at least a posted contentrequires the creation of a longer immutable sequential listing 700branch by the entity attempting the fraudulent at least a posted contentthan the branch being produced by the rest of the participants; as longas the entity creating the fraudulent at least a posted content islikely the only one with the incentive to create the branch containingthe fraudulent at least a posted content, the computational cost of thecreation of that branch may be practically infeasible, guaranteeing thevalidity of all at least a posted content in the immutable sequentiallisting 700.

Still referring to FIG. 7 , additional data linked to at least a postedcontent may be incorporated in sub-listings 708 in the immutablesequential listing 700; for instance, data may be incorporated in one ormore fields recognized by block chain protocols that permit a person orcomputer forming a at least a posted content to insert additional datain the immutable sequential listing 700. In some embodiments, additionaldata is incorporated in an unspendable at least a posted content field.For instance, the data may be incorporated in an OP_RETURN within theBITCOIN block chain. In other embodiments, additional data isincorporated in one signature of a multi-signature at least a postedcontent. In an embodiment, a multi-signature at least a posted contentis at least a posted content to two or more addresses. In someembodiments, the two or more addresses are hashed together to form asingle address, which is signed in the digital signature of the at leasta posted content. In other embodiments, the two or more addresses areconcatenated. In some embodiments, two or more addresses may be combinedby a more complicated process, such as the creation of a Merkle tree orthe like. In some embodiments, one or more addresses incorporated in themulti-signature at least a posted content are typical crypto-currencyaddresses, such as addresses linked to public keys as described above,while one or more additional addresses in the multi-signature at least aposted content contain additional data related to the at least a postedcontent; for instance, the additional data may indicate the purpose ofthe at least a posted content, aside from an exchange of virtualcurrency, such as the item for which the virtual currency was exchanged.In some embodiments, additional information may include networkstatistics for a given node of network, such as a distributed storagenode, e.g. the latencies to nearest neighbors in a network graph, theidentities or identifying information of neighboring nodes in thenetwork graph, the trust level and/or mechanisms of trust (e.g.certificates of physical encryption keys, certificates of softwareencryption keys, (in non-limiting example certificates of softwareencryption may indicate the firmware version, manufacturer, hardwareversion and the like), certificates from a trusted third party,certificates from a decentralized anonymous authentication procedure,and other information quantifying the trusted status of the distributedstorage node) of neighboring nodes in the network graph, IP addresses,GPS coordinates, and other information informing location of the nodeand/or neighboring nodes, geographically and/or within the networkgraph. In some embodiments, additional information may include historyand/or statistics of neighboring nodes with which the node hasinteracted. In some embodiments, this additional information may beencoded directly, via a hash, hash tree or other encoding.

With continued reference to FIG. 7 , in some embodiments, virtualcurrency is traded as a crypto currency. In one embodiment, a cryptocurrency is a digital, currency such as Bitcoins, Peercoins, Namecoins,and Litecoins. Crypto-currency may be a clone of anothercrypto-currency. The crypto-currency may be an “alt-coin.”Crypto-currency may be decentralized, with no particular entitycontrolling it; the integrity of the crypto-currency may be maintainedby adherence by its participants to established protocols for exchangeand for production of new currency, which may be enforced by softwareimplementing the crypto-currency. Crypto currency may be centralized,with its protocols enforced or hosted by a particular entity. Forinstance, crypto currency may be maintained in a centralized ledger, asin the case of the XRP currency of Ripple Labs, Inc., of San Francisco,Calif. In lieu of a centrally controlling authority, such as a nationalbank, to manage currency values, the number of units of a particularcrypto-currency may be limited; the rate at which units ofcrypto-currency enter the market may be managed by a mutuallyagreed-upon process, such as creating new units of currency whenmathematical puzzles are solved, the degree of difficulty of the puzzlesbeing adjustable to control the rate at which new units enter themarket. Mathematical puzzles may be the same as the algorithms used tomake productions of sub-listings 708 in a block chain computationallychallenging; the incentive for producing sub-listings 708 may includethe grant of new crypto currency to the miners. Quantities of cryptocurrency may be exchanged using at least a posted content as describedabove.

Referring now to FIG. 8 , an exemplary embodiment of neural network 800is illustrated. A neural network 800 also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes 804, one or more intermediate layers 808, and an output layer ofnodes 812. Connections between nodes may be created via the process of“training” the network, in which elements from a training dataset areapplied to the input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning. Connections may run solely from input nodes toward outputnodes in a “feed-forward” network or may feed outputs of one layer backto inputs of the same or a different layer in a “recurrent network.”

Referring now to FIG. 9 , an exemplary embodiment of a node of a neuralnetwork 900 is illustrated. A node may include, without limitation, aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally, or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above.

Referring now to FIG. 10 , an exemplary embodiment of a machine-learningmodule 1000 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 1004 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 1008 given data provided as inputs1012; this is in contrast to a non-machine learning software programwhere the commands to be executed are determined in advance by a userand written in a programming language.

Still referring to FIG. 10 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 1004 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 1004 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 1004 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 1004 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 1004 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 1004 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data1004 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively, or additionally, and continuing to refer to FIG. 10 ,training data 1004 may include one or more elements that are notcategorized; that is, training data 1004 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 1004 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 1004 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 1004 used by machine-learning module 1000 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure.

Further referring to FIG. 10 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 1016. Training data classifier 1016 may include a“classifier,” which as used in this disclosure is a machine-learningmodel as defined below, such as a mathematical model, neural net, orprogram generated by a machine learning algorithm known as a“classification algorithm,” as described in further detail below, thatsorts inputs into categories or bins of data, outputting the categoriesor bins of data and/or labels associated therewith. A classifier may beconfigured to output at least a datum that labels or otherwiseidentifies a set of data that are clustered together, found to be closeunder a distance metric as described below, or the like.Machine-learning module 1000 may generate a classifier using aclassification algorithm, defined as a process whereby a computingdevice and/or any module and/or component operating thereon derives aclassifier from training data 1004. Classification may be performedusing, without limitation, linear classifiers such as without limitationlogistic regression and/or naive Bayes classifiers, nearest neighborclassifiers such as k-nearest neighbors classifiers, support vectormachines, least squares support vector machines, fisher's lineardiscriminant, quadratic classifiers, decision trees, boosted trees,random forest classifiers, learning vector quantization, and/or neuralnetwork-based classifiers. As a non-limiting example, training dataclassifier 1016 may classify elements of training data to types ofdonors, types of recipients, types of medication, types of information,and the like.

Still referring to FIG. 10 , machine-learning module 1000 may beconfigured to perform a lazy-learning process 1020 and/or protocol,which may alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 1004.Heuristic may include selecting some number of highest-rankingassociations and/or training data 1004 elements. Lazy learning mayimplement any suitable lazy learning algorithm, including withoutlimitation a K-nearest neighbors algorithm, a lazy naïve Bayesalgorithm, or the like; persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various lazy-learningalgorithms that may be applied to generate outputs as described in thisdisclosure, including without limitation lazy learning applications ofmachine-learning algorithms as described in further detail below.

Alternatively, or additionally, and with continued reference to FIG. 10, machine-learning processes as described in this disclosure may be usedto generate machine-learning models 1024. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 1024 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 1024 may be generated by creating an artificialneural network, such as a convolutional neural network including aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 1004set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 10 , machine-learning algorithms may include atleast a supervised machine-learning process 1028. At least a supervisedmachine-learning process 1028, as defined herein, include algorithmsthat receive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude inputs as described in this disclosure as inputs, outputs asdescribed in this disclosure as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 1004. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process1028 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 10 , machine learning processes may include atleast an unsupervised machine-learning processes 1032. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 10 , machine-learning module 1000 may bedesigned and configured to create a machine-learning model 1024 usingtechniques for development of linear regression models. Linearregression models may include ordinary least squares regression, whichaims to minimize the square of the difference between predicted outcomesand actual outcomes according to an appropriate norm for measuring sucha difference (e.g., a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g., a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 10 , machine-learning algorithms mayinclude, without limitation, linear discriminant analysis.Machine-learning algorithm may include quadratic discriminate analysis.Machine-learning algorithms may include kernel ridge regression.Machine-learning algorithms may include support vector machines,including without limitation support vector classification-basedregression processes. Machine-learning algorithms may include stochasticgradient descent algorithms, including classification and regressionalgorithms based on stochastic gradient descent. Machine-learningalgorithms may include nearest neighbors algorithms. Machine-learningalgorithms may include various forms of latent space regularization suchas variational regularization. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 11 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 1100 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 1100 includes a processor 1104 and a memory1108 that communicate with each other, and with other components, via abus 1112. Bus 1112 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 1104 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 1104 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 1104 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC).

Memory 1108 may include various components (e.g., machine-readablemedia) including, but not limited to, a random-access memory component,a read only component, and any combinations thereof. In one example, abasic input/output system 1116 (BIOS), including basic routines thathelp to transfer information between elements within computer system1100, such as during start-up, may be stored in memory 1108. Memory 1108may also include (e.g., stored on one or more machine-readable media)instructions (e.g., software) 1120 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 1108 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 1100 may also include a storage device 1124. Examples ofa storage device (e.g., storage device 1124) include, but are notlimited to, a hard disk drive, a magnetic disk drive, an optical discdrive in combination with an optical medium, a solid-state memorydevice, and any combinations thereof. Storage device 1124 may beconnected to bus 1112 by an appropriate interface (not shown). Exampleinterfaces include, but are not limited to, SCSI, advanced technologyattachment (ATA), serial ATA, universal serial bus (USB), IEEE 1394(FIREWIRE), and any combinations thereof. In one example, storage device1124 (or one or more components thereof) may be removably interfacedwith computer system 1100 (e.g., via an external port connector (notshown)). Particularly, storage device 1124 and an associatedmachine-readable medium 1128 may provide nonvolatile and/or volatilestorage of machine-readable instructions, data structures, programmodules, and/or other data for computer system 1100. In one example,software 1120 may reside, completely or partially, withinmachine-readable medium 1128. In another example, software 1120 mayreside, completely or partially, within processor 1104.

Computer system 1100 may also include an input device 1132. In oneexample, a user of computer system 1100 may enter commands and/or otherinformation into computer system 1100 via input device 1132. Examples ofan input device 1132 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 1132may be interfaced to bus 1112 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 1112, and any combinations thereof. Input device 1132may include a touch screen interface that may be a part of or separatefrom display 1136, discussed further below. Input device 1132 may beutilized as a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 1100 via storage device 1124 (e.g., a removable disk drive, aflash drive, etc.) and/or network interface device 1140. A networkinterface device, such as network interface device 1140, may be utilizedfor connecting computer system 1100 to one or more of a variety ofnetworks, such as network 1144, and one or more remote devices 1148connected thereto. Examples of a network interface device include, butare not limited to, a network interface card (e.g., a mobile networkinterface card, a LAN card), a modem, and any combination thereof.Examples of a network include, but are not limited to, a wide areanetwork (e.g., the Internet, an enterprise network), a local areanetwork (e.g., a network associated with an office, a building, a campusor other relatively small geographic space), a telephone network, a datanetwork associated with a telephone/voice provider (e.g., a mobilecommunications provider data and/or voice network), a direct connectionbetween two computing devices, and any combinations thereof. A network,such as network 1144, may employ a wired and/or a wireless mode ofcommunication. In general, any network topology may be used. Information(e.g., data, software 1120, etc.) may be communicated to and/or fromcomputer system 1100 via network interface device 1140.

Computer system 1100 may further include a video display adapter 1152for communicating a displayable image to a display device, such asdisplay device 1136. Examples of a display device include, but are notlimited to, a liquid crystal display (LCD), a cathode ray tube (CRT), aplasma display, a light emitting diode (LED) display, and anycombinations thereof. Display adapter 1152 and display device 1136 maybe utilized in combination with processor 1104 to provide graphicalrepresentations of aspects of the present disclosure. In addition to adisplay device, computer system 1100 may include one or more otherperipheral output devices including, but not limited to, an audiospeaker, a printer, and any combinations thereof. Such peripheral outputdevices may be connected to bus 1112 via a peripheral interface 1156.Examples of a peripheral interface include, but are not limited to, aserial port, a USB connection, a FIREWIRE connection, a parallelconnection, and any combinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve systems andmethods according to the present disclosure. Accordingly, thisdescription is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for redistributing medication, thesystem comprising a computing device designed and configured to: receivea communication regarding a medication donation from a donor, whereinthe communication comprises donor medication information regarding themedication donation; verify the medication donation as a function ofactual medication information, wherein verifying the medication donationcomprises: collecting the actual medication information for themedication donation using a sensor, wherein the sensor comprises acamera; verifying the identity of the medication donation; and verifyingthe integrity of the medication donation; and enter the actualmedication information for the medication donation into the medicationdatabase as a function of verifying the medication donation.
 2. Thesystem of claim 1, wherein collecting actual medication information forthe medication donation comprises receiving an image of the medicationdonation from the camera.
 3. The system of claim 2, wherein collectingactual medication information for the medication donation furthercomprises: training an image classifying machine learning model usingtraining data and a machine learning algorithm, wherein the trainingdata comprises medication image data correlated with medicationinformation data; and generating the actual medication information forthe medication donation using the trained image classifying machinelearning model.
 4. The system of claim 2, wherein verifying the identityof the medication donation comprises performing optical characterrecognition on the image of the medication donation to extractmachine-encoded text from the image of the medication donation.
 5. Thesystem of claim 1, wherein the computing device is further configuredto: create a verification record comprising a verification status forthe medication donation; and store the verification record on animmutable sequential listing.
 6. The system of claim 1, whereinverifying the medication donation comprises checking the medicationdonation for tampering, wherein checking the medication donation fortampering comprises determining a tampering status.
 7. The system ofclaim 6, wherein the computing device is further configured to: create averification record comprising a verification status for the medicationdonation and a tampering status for the medication donation; and storethe verification record on an immutable sequential listing.
 8. Thesystem of claim 6, wherein collecting actual medication information forthe medication donation comprises: receiving a first image of anexterior packaging the medication donation from the camera; andcollecting a first set of medication information from the first image.9. The system of claim 8, wherein: collecting actual medicationinformation for the medication donation further comprises: receiving asecond image of an interior packaging of the medication donation fromthe camera; and collecting a second set of medication information fromthe second image; and checking the medication donation for tamperingcomprises comparing the first set of medication information to thesecond set of medication information.
 10. The system of claim 9, whereinthe interior packaging comprises a blister pack.
 11. A method forredistributing medication, the method comprising: receiving, by thecomputing device, a communication regarding a medication donation from adonor, wherein the communication comprises donor medication informationregarding the medication donation; verifying, by the computing device,the medication donation as a function of actual medication information,wherein verifying the medication donation comprises: collecting theactual medication information for the medication donation using asensor, wherein the sensor comprises a camera verifying the identity ofthe medication donation; and verifying the integrity of the medicationdonation; and entering, by the computing device, the actual medicationinformation for the medication donation into the medication database asa function of verifying the medication donation.
 12. The method of claim11, wherein collecting actual medication information for the medicationdonation comprises receiving an image of the medication donation fromthe camera.
 13. The method of claim 12, wherein collecting actualmedication information for the medication donation further comprises:training an image classifying machine learning model using training dataand a machine learning algorithm, wherein the training data comprisesmedication image data correlated with medication information data; andgenerating the actual medication information for the medication donationusing the trained image classifying machine learning model.
 14. Themethod of claim 12, wherein verifying the identity of the medicationdonation comprises performing optical character recognition on the imageof the medication donation to extract machine-encoded text from theimage of the medication donation.
 15. The method of claim 11, furthercomprising: creating, by the computing device, a verification recordcomprising a verification status for the medication donation; andstoring, by the computing device, the verification record on animmutable sequential listing.
 16. The method of claim 11, whereinverifying the medication donation comprises checking the medicationdonation for tampering, wherein checking the medication donation fortampering comprises determining a tampering status.
 17. The method ofclaim 16, further comprising: creating, by the computing device, averification record comprising a verification status for the medicationdonation and a tampering status for the medication donation; andstoring, by the computing device, the verification record on animmutable sequential listing.
 18. The method of claim 16, whereincollecting actual medication information for the medication donationcomprises: receiving a first image of an exterior packaging themedication donation from the camera; and collecting a first set ofmedication information from the first image.
 19. The method of claim 18,wherein: collecting actual medication information for the medicationdonation further comprises: receiving a second image of an interiorpackaging of the medication donation from the camera; and collecting asecond set of medication information from the second image; and checkingthe medication donation for tampering comprises comparing the first setof medication information to the second set of medication information.20. The method of claim 19, wherein the interior packaging comprises ablister pack.